Upload 12 files

fc3d85b verified 5 months ago

17.5 kB

	---
	base_model: klue/roberta-base
	datasets: []
	language: []
	library_name: sentence-transformers
	metrics:
	- pearson_cosine
	- spearman_cosine
	- pearson_manhattan
	- spearman_manhattan
	- pearson_euclidean
	- spearman_euclidean
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:574417
	- loss:MultipleNegativesRankingLoss
	- loss:CosineSimilarityLoss
	widget:
	- source_sentence: 이집트 대통령 선거에서 가까운 여론조사
	sentences:
	- 알 카에다 충돌, 폭발로 예멘에서 35명의 군인이 사망
	- '보도자료 : 예멘 대통령 선거'
	- 반 파이프에 스케이트보드를 신은 남자
	- source_sentence: 한 소년이 팽창식 슬라이드를 내려간다.
	sentences:
	- 빨간 옷을 입은 소년이 부풀릴 수 있는 놀이기구를 타고 내려간다.
	- 새들이 물속에서 헤엄치고 있다.
	- 여자는 녹색 후추를 썰었다.
	- source_sentence: 비상 차량들이 현장에 있다.
	sentences:
	- 구급차와 소방차가 현장에서 도움을 주려고 한다.
	- 유물을 보는 사람들이 있다.
	- 구급차와 소방차에 불이 붙었다.
	- source_sentence: 그들은 서로 가까이 있지 않다.
	sentences:
	- 그 품질은 레이저에 가깝다.
	- 그들은 샤토와 매우 가깝다.
	- 그들은 샤토와 서로 어느 정도 떨어져 있다.
	- source_sentence: 딱딱한 모자를 쓴 남자가 건물 프레임 앞에 주차된 빨간 트럭의 침대를 쳐다본다.
	sentences:
	- 남자가 자고 있다.
	- 사람들이 말하고 있다.
	- 한 남자가 트럭을 보고 있다.
	model-index:
	- name: SentenceTransformer based on klue/roberta-base
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev
	type: sts-dev
	metrics:
	- type: pearson_cosine
	value: 0.8650328554572645
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8667952293243948
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8558437246473041
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.860673936504169
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8562228685196989
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8612884653822855
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.830160661850442
	name: Pearson Dot
	- type: spearman_dot
	value: 0.8275972106510755
	name: Spearman Dot
	- type: pearson_max
	value: 0.8650328554572645
	name: Pearson Max
	- type: spearman_max
	value: 0.8667952293243948
	name: Spearman Max
	---

	# SentenceTransformer based on klue/roberta-base

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [klue/roberta-base](https://huggingface.co/klue/roberta-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

	## Model Details

	### Model Description
	- Model Type: Sentence Transformer
	- Base model: [klue/roberta-base](https://huggingface.co/klue/roberta-base) <!-- at revision 02f94ba5e3fcb7e2a58a390b8639b0fac974a8da -->
	- Maximum Sequence Length: 128 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	<!-- - Language: Unknown -->
	<!-- - License: Unknown -->

	### Model Sources

	- Documentation: [Sentence Transformers Documentation](https://sbert.net)
	- Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
	- Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

	### Full Model Architecture

	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel
	(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	)
	```

	## Usage

	### Direct Usage (Sentence Transformers)

	First install the Sentence Transformers library:

	```bash
	pip install -U sentence-transformers
	```

	Then you can load this model and run inference.
	```python
	from sentence_transformers import SentenceTransformer

	# Download from the 🤗 Hub
	model = SentenceTransformer("sentence_transformers_model_id")
	# Run inference
	sentences = [
	'딱딱한 모자를 쓴 남자가 건물 프레임 앞에 주차된 빨간 트럭의 침대를 쳐다본다.',
	'한 남자가 트럭을 보고 있다.',
	'남자가 자고 있다.',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 768]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings, embeddings)
	print(similarities.shape)
	# [3, 3]
	```

	<!--
	### Direct Usage (Transformers)

	<details><summary>Click to see the direct usage in Transformers</summary>

	</details>
	-->

	<!--
	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
	-->

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	## Evaluation

	### Metrics

	#### Semantic Similarity
	* Dataset: `sts-dev`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:-------------------\|:-----------\|
	\| pearson_cosine \| 0.865 \|
	\| spearman_cosine \| 0.8668 \|
	\| pearson_manhattan \| 0.8558 \|
	\| spearman_manhattan \| 0.8607 \|
	\| pearson_euclidean \| 0.8562 \|
	\| spearman_euclidean \| 0.8613 \|
	\| pearson_dot \| 0.8302 \|
	\| spearman_dot \| 0.8276 \|
	\| pearson_max \| 0.865 \|
	\| spearman_max \| 0.8668 \|

	<!--
	## Bias, Risks and Limitations

	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
	-->

	<!--
	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
	-->

	## Training Details

	### Training Datasets

	#### Unnamed Dataset


	* Size: 568,640 training samples
	* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence_0 \| sentence_1 \| sentence_2 \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 19.21 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 18.29 tokens</li><li>max: 93 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 14.61 tokens</li><li>max: 57 tokens</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \| sentence_2 \|
	\|:----------------------------------------\|:-------------------------------------------------\|:--------------------------------------\|
	\| <code>발생 부하가 함께 5% 적습니다.</code> \| <code>발생 부하의 5% 감소와 함께 11.</code> \| <code>발생 부하가 5% 증가합니다.</code> \|
	\| <code>어떤 행사를 위해 음식과 옷을 배급하는 여성들.</code> \| <code>여성들은 음식과 옷을 나눠줌으로써 난민들을 돕고 있다.</code> \| <code>여자들이 사막에서 오토바이를 운전하고 있다.</code> \|
	\| <code>어린 아이들은 그 지식을 얻을 필요가 있다.</code> \| <code>응, 우리 젊은이들 중 많은 사람들이 그걸 배워야 할 것 같아.</code> \| <code>젊은 사람들은 배울 필요가 없다.</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

	#### Unnamed Dataset


	* Size: 5,777 training samples
	* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence_0 \| sentence_1 \| label \|
	\|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 3 tokens</li><li>mean: 17.61 tokens</li><li>max: 65 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 17.66 tokens</li><li>max: 76 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \| label \|
	\|:-----------------------------------------\|:------------------------------------------\|:--------------------------------\|
	\| <code>몰디브 대통령이 경찰의 반란 이후 사임하고, 시위</code> \| <code>몰디브 대통령이 몇 주 동안의 시위 끝에 그만두다.</code> \| <code>0.6799999999999999</code> \|
	\| <code>사자가 밀폐된 지역을 걷고 있다.</code> \| <code>사자가 주위를 돌아다니고 있다.</code> \| <code>0.52</code> \|
	\| <code>한 소년이 노래를 부르고 피아노를 치고 있다.</code> \| <code>한 소년이 피아노를 치고 있다.</code> \| <code>0.6</code> \|
	* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
	```json
	{
	"loss_fct": "torch.nn.modules.loss.MSELoss"
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `eval_strategy`: steps
	- `num_train_epochs`: 5
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: round_robin

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: steps
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 8
	- `per_device_eval_batch_size`: 8
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_steps`: None
	- `learning_rate`: 5e-05
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1
	- `num_train_epochs`: 5
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.0
	- `warmup_steps`: 0
	- `log_level`: passive
	- `log_level_replica`: warning
	- `log_on_each_node`: True
	- `logging_nan_inf_filter`: True
	- `save_safetensors`: True
	- `save_on_each_node`: False
	- `save_only_model`: False
	- `restore_callback_states_from_checkpoint`: False
	- `no_cuda`: False
	- `use_cpu`: False
	- `use_mps_device`: False
	- `seed`: 42
	- `data_seed`: None
	- `jit_mode_eval`: False
	- `use_ipex`: False
	- `bf16`: False
	- `fp16`: False
	- `fp16_opt_level`: O1
	- `half_precision_backend`: auto
	- `bf16_full_eval`: False
	- `fp16_full_eval`: False
	- `tf32`: None
	- `local_rank`: 0
	- `ddp_backend`: None
	- `tpu_num_cores`: None
	- `tpu_metrics_debug`: False
	- `debug`: []
	- `dataloader_drop_last`: False
	- `dataloader_num_workers`: 0
	- `dataloader_prefetch_factor`: None
	- `past_index`: -1
	- `disable_tqdm`: False
	- `remove_unused_columns`: True
	- `label_names`: None
	- `load_best_model_at_end`: False
	- `ignore_data_skip`: False
	- `fsdp`: []
	- `fsdp_min_num_params`: 0
	- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
	- `fsdp_transformer_layer_cls_to_wrap`: None
	- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
	- `deepspeed`: None
	- `label_smoothing_factor`: 0.0
	- `optim`: adamw_torch
	- `optim_args`: None
	- `adafactor`: False
	- `group_by_length`: False
	- `length_column_name`: length
	- `ddp_find_unused_parameters`: None
	- `ddp_bucket_cap_mb`: None
	- `ddp_broadcast_buffers`: False
	- `dataloader_pin_memory`: True
	- `dataloader_persistent_workers`: False
	- `skip_memory_metrics`: True
	- `use_legacy_prediction_loop`: False
	- `push_to_hub`: False
	- `resume_from_checkpoint`: None
	- `hub_model_id`: None
	- `hub_strategy`: every_save
	- `hub_private_repo`: False
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `eval_do_concat_batches`: True
	- `fp16_backend`: auto
	- `push_to_hub_model_id`: None
	- `push_to_hub_organization`: None
	- `mp_parameters`:
	- `auto_find_batch_size`: False
	- `full_determinism`: False
	- `torchdynamo`: None
	- `ray_scope`: last
	- `ddp_timeout`: 1800
	- `torch_compile`: False
	- `torch_compile_backend`: None
	- `torch_compile_mode`: None
	- `dispatch_batches`: None
	- `split_batches`: None
	- `include_tokens_per_second`: False
	- `include_num_input_tokens_seen`: False
	- `neftune_noise_alpha`: None
	- `optim_target_modules`: None
	- `batch_eval_metrics`: False
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: round_robin

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| sts-dev_spearman_max \|
	\|:------:\|:----:\|:-------------:\|:--------------------:\|
	\| 0.3458 \| 500 \| 0.4123 \| - \|
	\| 0.6916 \| 1000 \| 0.3009 \| 0.8365 \|
	\| 1.0007 \| 1447 \| - \| 0.8610 \|
	\| 1.0367 \| 1500 \| 0.259 \| - \|
	\| 1.3824 \| 2000 \| 0.1301 \| 0.8580 \|
	\| 1.7282 \| 2500 \| 0.0898 \| - \|
	\| 2.0007 \| 2894 \| - \| 0.8668 \|


	### Framework Versions
	- Python: 3.11.9
	- Sentence Transformers: 3.0.1
	- Transformers: 4.41.2
	- PyTorch: 2.2.2+cu121
	- Accelerate: 0.31.0
	- Datasets: 2.20.0
	- Tokenizers: 0.19.1

	## Citation

	### BibTeX

	#### Sentence Transformers
	```bibtex
	@inproceedings{reimers-2019-sentence-bert,
	title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
	author = "Reimers, Nils and Gurevych, Iryna",
	booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
	month = "11",
	year = "2019",
	publisher = "Association for Computational Linguistics",
	url = "https://arxiv.org/abs/1908.10084",
	}
	```

	#### MultipleNegativesRankingLoss
	```bibtex
	@misc{henderson2017efficient,
	title={Efficient Natural Language Response Suggestion for Smart Reply},
	author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
	year={2017},
	eprint={1705.00652},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```

	<!--
	## Glossary

	Clearly define terms in order to be accessible across audiences.
	-->

	<!--
	## Model Card Authors

	Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.
	-->

	<!--
	## Model Card Contact

	Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.
	-->